There are many other explainable ML methods that operate on trained models directly such as partial dependence, ALE, and ICE plots [1]}, [2]}, [3]}. Surrogate models and explanatory techniques that operate directly on trained models can also be combined, for instance by using partial dependence, ICE, and surrogate decision trees to investigate and confirm modeled interactions [4]}. In Figure REF , an unconstrained GBM, \(g_{\text{GBM}}\) , models a known signal generating function \(f\) :
\(f(\mathbf {X}) = \left\lbrace \begin{array}{ll}1 & \mbox{if } X_{\text{num}1} * X_{\text{num}4} + |X_{\text{num}8}| * X_{\text{num}9}^2 + e \ge 0.42 \\0 & \mbox{if } X_{\text{num}1} * X_{\text{num}4} + |X_{\text{num}8}| * X_{\text{num}9}^2 + e < 0.42\end{array}\right.\) 
